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Statistical modelling of neural networks in γ-spectrometry

Abstract : Layered Neural Networks are a class of models based on neural computation and have been applied to the measurement of uranium enrichment. The usual methods consider a limited number of X- and γ-ray peaks, and require calibrated instrumentation for each sample. Since the source-detector ensemble geometry conditions critically differ between such measurements, the spectral region of interest is normally reduced to improve the accuracy of such conventional methods by focusing on the KαX region where the three elementary components are present. Such measurements lead to the desired ratio. Experimental data have been used to study the performance of neural networks involving a Maximum Likelihood Method. The encoding of the data by a Neural Network approach is a promising method for the measurement of uranium 235U and 238U in infinitely thick samples.
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https://hal-cea.archives-ouvertes.fr/cea-02524817
Contributor : Marie-France Robbe <>
Submitted on : Monday, March 30, 2020 - 3:34:18 PM
Last modification on : Tuesday, April 28, 2020 - 11:28:10 AM

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Vincent Vigneron, Jean Morel, Marie-Christine Lépy, Jean-Marc Martinez. Statistical modelling of neural networks in γ-spectrometry. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Elsevier, 1996, 369 (2-3), pp.642-647. ⟨10.1016/S0168-9002(96)80068-4⟩. ⟨cea-02524817⟩

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